HMS - Harmful Brain Activity Classification
有害脑活动分类

From stethoscopes to tongue depressors, doctors rely on many tools to treat their patients. Physicians use electroencephalography with critically ill patients to detect seizures and other types of brain activity that can cause brain damage. You can learn about how doctors interpret these EEG signals in these videos:
从听诊器到舌压板，医生们依靠许多工具来治疗他们的患者。医生们使用脑电图技术来检测重症患者的癫痫发作和其他可能导致脑损伤的脑活动。您可以通过这些视频了解医生如何解读这些脑电图信号。
EEG Talk - ACNS Critical Care EEG Terminology 2021 (Part 1) (Part 2) (Part 3) (Part 4) (Part 5)
EEG Talk - ACNS 临床护理 EEG 术语2021（第一部分）（第二部分）（第三部分）（第四部分）（第五部分）

Currently, EEG monitoring relies solely on manual analysis by specialized neurologists. While invaluable, this labor-intensive process is a major bottleneck. Not only can it be time-consuming, but manual review of EEG recordings is also expensive, prone to fatigue-related errors, and suffers from reliability issues between different reviewers, even when those reviewers are experts.
目前，脑电图监测完全依赖专业神经学家的手工分析。尽管宝贵，但这种人工密集的过程是主要瓶颈。它不仅耗时，而且手动审查脑电图记录还很昂贵，容易出现因疲劳引起的错误，并且即使是专家，不同审查者之间也存在可靠性问题。

Competition host Sunstella Foundation was created in 2021 during the COVID pandemic to help minority graduate students in technology overcome challenges and celebrate their achievements. These students are vital to America's technology leadership and diversity. Through workshops, forums, and competitions, the Sunstella Foundation provides mentorship and career advice to support their success.
竞赛主办方Sunstella基金会成立于2021年的COVID大流行期间，旨在帮助科技领域的少数民族研究生克服挑战并庆祝他们的成就。这些学生对于美国的技术领导力和多样性至关重要。通过研讨会、论坛和竞赛，Sunstella基金会提供导师指导和职业建议，支持他们的成功。

Sunstella Foundation is joined by Persyst, Jazz Pharmaceuticals, and the Clinical Data Animation Center (CDAC), whose research aims to help people preserve and enhance brain health.
Sunstella基金会与Persyst、爵士制药和临床数据动画中心（CDAC）合作，他们的研究旨在帮助人们保护和提升大脑健康。

Your work in automating EEG analysis will help doctors and brain researchers detect seizures and other types of brain activity that can cause brain damage, so that they can give treatments more quickly and accurately. The algorithms developed in this contest may also help researchers who are working to develop drugs to treat and prevent seizures.
您在自动化脑电图分析方面的工作将帮助医生和脑科研究人员检测癫痫和其他可能导致脑损伤的脑活动类型，以便他们能够更快速、准确地进行治疗。在这个竞赛中开发的算法也可能帮助那些致力于开发治疗和预防癫痫药物的研究人员。

There are six patterns of interest for this competition: seizure (SZ), generalized periodic discharges (GPD), lateralized periodic discharges (LPD), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), or “other”. Detailed explanations of these patterns are available here.
这个竞赛有六种感兴趣的模式：癫痫发作（SZ），广泛周期性放电（GPD），侧化周期性放电（LPD），侧化节律性δ活动（LRDA），广泛节律性δ活动（GRDA），或者“其他”。这些模式的详细解释在这里提供。

The EEG segments used in this competition have been annotated, or classified, by a group of experts. In some cases experts completely agree about the correct label. On other cases the experts disagree. We call segments where there are high levels of agreement “idealized” patterns. Cases where ~1/2 of experts give a label as “other” and ~1/2 give one of the remaining five labels, we call “proto patterns”. Cases where experts are approximately split between 2 of the 5 named patterns, we call “edge cases”.
这个竞赛中使用的脑电图片段已经由一组专家进行了注释或分类。在某些情况下，专家对正确标签完全一致。在其他情况下，专家意见不一致。我们将存在高度一致性的片段称为“理想化”模式。当大约一半的专家给出“其他”标签，而另一半给出剩下的五个标签之一时，我们称之为“原型模式”。当专家在五个命名模式中大致分为两派时，我们称之为“边缘情况”。

Examples of EEG Patterns with Different Levels of Expert Agreement:
专家一致认为具有不同水平的脑电图模式的例子：

Please refer to Data tab for full screen PDF page of each subfigure.
请参考数据选项卡以获取每个子图的全屏PDF页面。

This figure shows selected examples of EEG patterns with different level of agreement. Rows are structured with the 1st row seizure, 2nd row LPDs, 3rd row GPDs, 4th row LRDA, and 5th row GRDA. Column-wise, examples of idealized forms of patterns are in the 1st column (A). These are patterns with uniform expert agreement. The 2nd column (B) are proto or partially formed patterns. About half of raters labeled these as one IIIC pattern and the other half labeled “Other”. The 3rd and 4th columns (C, D) are edge cases (about half of raters labeled these one IIIC pattern and half labeled them as another IIIC pattern).
这个图示展示了具有不同一致性水平的选定脑电图模式的示例。行按照以下结构排列：第一行是癫痫发作，第二行是局限性脑电放电（LPDs），第三行是全局性脑电放电（GPDs），第四行是局限性脑电放电伴随着反应性脑电抑制（LRDA），第五行是全局性脑电放电伴随着反应性脑电抑制（GRDA）。按列划分，第一列（A）是理想化形式的模式示例，这些模式具有一致的专家意见。第二列（B）是原型或部分形成的模式，大约一半的评估者将其标记为一种IIIC模式，另一半标记为“其他”。第三列和第四列（C，D）是边缘情况，大约一半的评估者将其标记为一种IIIC模式，另一半将其标记为另一种IIIC模式。

For B-1 there is rhythmic delta activity with some admixed sharp discharges within the 10 second raw EEG, and the spectrogram shows that this segment may belong to the tail end of a seizure, thus disagreement between SZ and “Other” makes sense. B-2 shows frontal lateralized sharp transients at ~1Hz, but they have a reversed polarity, suggesting they may be coming from a non-cerebral source, thus the split between LPD and “Other” (artifact) makes sense. B-3 has diffused semi-rhythmic delta background with poorly formed low amplitude generalized periodic discharges with s shifting morphology making it a proto-GPD type pattern. B-4 shows semi-rhythmic delta activity with unstable morphology over the right hemisphere, a proto-LRDA pattern. B-5 shows a few waves of rhythmic delta activity with an unstable morphology and is poorly sustained, a proto-GRDA. C-1 shows 2Hz LPDs showing an evolution with increasing amplitude evolving underlying rhythmic activity, a pattern between LPDs and the beginning of a seizure, an edge-case. D-1 shows abundant GPDs on top of a suppressed background with frequency of 1-2Hz. The average over the 10-seconds is close to 1.5Hz, suggesting a seizure, another edge case. C-2 is split between LPDs and GPDs. The amplitude of the periodic discharges is higher over the right, but a reflection is also seen on the left. D-2 is tied between LPDs and LRDA. It shares some features of both; in the temporal derivations it looks more rhythmic whereas in the parasagittal derivations it looks periodic. C-3 is split between GPDs and LRDA. The ascending limb of the delta waves have a sharp morphology, and these periodic discharges are seen on both sides. The rhythmic delta appears to be of higher amplitude over the left, but there is some reflection of the activity on the left. D-3 is split between GPDs and GRDA. The ascending limb of the delta wave has a sharp morphology and there is asymmetry in slope between ascending and descending limbs making it an edge case. C-4 is split between LRDA and seizure. It shows 2Hz LRDA on the left, and the spectrogram shows that this segment may belong to the tail end of a seizure, an edge-case. D-4 is split between LRDA and GRDA. The rhythmic delta appears to be of higher amplitude over the left, but there is some reflection of the activity on the right. C-5 is split between GRDA and seizure. It shows potentially evolving rhythmic delta activity with poorly formed embedded epileptiform discharges, a pattern between GRDA and seizure, an edge-case. D-5 is split between GRDA and LPDs. There is generalized rhythmic delta activity, while the activity on the right is somewhat higher amplitude and contains poorly formed epileptiform discharges suggestive of LPDs, an edge-case. Note: Recording regions of the EEG electrodes are abbreviated as LL = left lateral; RL = right lateral; LP = left parasagittal; RP = right parasagittal.
对于B-1，10秒的原始脑电图中有节律性的δ波活动，其中夹杂着一些尖锐的放电，频谱图显示这一段可能属于癫痫发作的尾部，因此SZ和“其他”之间的不一致是有道理的。B-2显示出前额侧化的尖锐瞬变，频率约为1Hz，但它们的极性相反，表明它们可能来自非脑源，因此LPD和“其他”（伪影）之间的分离是有道理的。B-3具有弥散的半节律性δ波背景，伴有形态不规则、幅度低的广泛周期性放电，形态变化使其成为原型GPD类型的模式。B-4显示出不稳定形态的半节律性δ波活动在右半球上，是原型LRDA模式。B-5显示出几个波动的节律性δ波活动，形态不稳定且持续性差，是原型GRDA。C-1显示出2Hz的LPD，随着幅度增加而演变为基础节律活动，是LPD和癫痫开始之间的一种模式，边缘情况。D-1显示出丰富的GPD，频率为1-2Hz，位于抑制性背景之上。10秒的平均值接近1。5Hz，暗示发作，另一个边缘情况。C-2在LPDs和GPDs之间分裂。周期性放电的振幅在右侧较高，但左侧也有反射。D-2在LPDs和LRDA之间分裂。它具有两者的一些特征；在颞部导联中，它看起来更有节奏感，而在矢状导联中，它呈周期性。C-3在GPDs和LRDA之间分裂。δ波的上升支具有尖锐的形态，并且这些周期性放电在两侧都可见。节奏性的δ波在左侧的振幅似乎更高，但左侧也有一些活动的反射。D-3在GPDs和GRDA之间分裂。δ波的上升支具有尖锐的形态，并且上升支和下降支之间的斜率存在不对称，使其成为一个边缘情况。C-4在LRDA和发作之间分裂。左侧显示2Hz的LRDA，频谱图显示该段可能属于发作的尾部，一个边缘情况。D-4在LRDA和GRDA之间分裂。节奏性的δ波在左侧的振幅似乎更高，但右侧也有一些活动的反射。 C-5在GRDA和癫痫之间分裂。它显示出潜在的演变中的节律性δ活动，其中嵌入了形态不良的癫痫放电，这是GRDA和癫痫之间的一种模式，一个边缘案例。D-5在GRDA和LPDs之间分裂。有广泛的节律性δ活动，而右侧的活动幅度稍高，并含有形态不良的癫痫放电，提示为LPDs，一个边缘案例。注意：EEG电极的记录区域缩写为LL = 左侧；RL = 右侧；LP = 左侧剖面；RP = 右侧剖面。

Submissions are evaluated on the Kullback Liebler divergence between the predicted probability and the observed target.
提交的结果将根据预测概率与观察目标之间的Kullback Liebler差异进行评估。

Submission File 提交文件

For each eeg_id in the test set, you must predict a probability for each of the vote columns. The file should contain a header and have the following format:
对于测试集中的每个 eeg_id ，您必须预测 vote 列的每个概率。文件应包含标题，并具有以下格式：

eeg_id,seizure_vote,lpd_vote,gpd_vote,lrda_vote,grda_vote,other_vote
eeg_id，癫痫投票，低频投票，高频投票，低频右侧投票，高频右侧投票，其他投票
0,0.166,0.166,0.167,0.167,0.167,0.167
1,0.166,0.166,0.167,0.167,0.167,0.167
etc. 等等。

Your total predicted probabilities for each row must sum to one or your submission will fail.
您每行的总预测概率必须总和为1，否则提交将失败。

• January 8, 2024 - Start Date.
  2024年1月8日 - 开始日期。

• April 1, 2024 - Entry Deadline. You must accept the competition rules before this date in order to compete.
  2024年4月1日-报名截止。您必须在此日期之前接受比赛规则才能参加比赛。

• April 1, 2024 - Team Merger Deadline. This is the last day participants may join or merge teams.
  2024年4月1日 - 团队合并截止日期。这是参与者加入或合并团队的最后一天。

• April 8, 2024 - Final Submission Deadline.
  2024年4月8日 - 最后提交截止日期。

All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
除非另有说明，所有截止日期均为UTC时间当天的晚上11:59，比赛组织者保留根据需要更新比赛时间表的权利。

• 1st Place - $20,000
  第一名 - 20,000美元
• 2nd Place - $12,000
  第二名 - 12,000美元
• 3rd Place - $7,000
  第三名 - 7000美元
• 4th Place - $6,000
• 5th Place - $5,000
  第五名 - 5000美元

This is a Code Competition
这是一个代码竞赛

Submissions to this competition must be made through Notebooks. In order for the "Submit" button to be active after a commit, the following conditions must be met:
参赛作品必须通过笔记本提交。为了在提交后激活“提交”按钮，必须满足以下条件：

• CPU Notebook <= 9 hours run-time
  CPU笔记本电脑 <= 9小时运行时间
• GPU Notebook <= 9 hours run-time
  GPU笔记本电脑 <= 9小时运行时间
• Internet access disabled
  网络访问已禁用
• Freely & publicly available external data is allowed, including pre-trained models
  允许使用自由和公开可用的外部数据，包括预训练模型
• Submission file must be named submission.csv
  提交文件必须命名为 submission.csv

Please see the Code Competition FAQ for more information on how to submit. And review the code debugging doc if you are encountering submission errors.
请查看代码竞赛常见问题解答，了解如何提交。如果遇到提交错误，请查阅代码调试文档。

We gratefully acknowledge the work of the experts from the Critical Care EEG Monitoring Research Consortium (CCEMRC), who provided the labels for model training and evaluation, and to others who contributed to the scientific work that enabled this competition. Specifically, we acknowledge the contributions of (numbers refer to institutional affiliations in the list below):
我们衷心感谢来自危重病护理脑电图监测研究联盟（CCEMRC）的专家们为模型的训练和评估提供的标签，以及其他为使这次竞赛成为可能的科学工作的贡献者。具体来说，我们感谢以下机构的贡献（数字指下面列表中的机构隶属）：

Jin Jing^1,2, Wendong Ge, Aaron F. Struck^3,4 , Marta Bento Fernandes^1,2, Shenda Hong⁵, Sungtae An⁷, Safoora Fatima³, Aline Herlopian⁸, Ioannis Karakis⁹, Jonathan J. Halford¹⁰, Marcus Ng¹¹, Emily L. Johnson¹², Brian Appavu¹³, Rani A. Sarkis¹⁴, Gamaleldin Osman¹⁵, Peter W. Kaplan¹², Monica B. Dhakar¹⁶, Lakshman Arcot Jayagopal¹⁷, Zubeda Sheikh¹⁸, Olha Taraschenko¹⁷, Sarah Schmitt¹⁰, Hiba A. Haider¹⁹, Jennifer A. Kim⁸, Christa B. Swisher²⁰, Nicolas Gaspard²¹, Mackenzie C. Cervenka¹², Andres Rodriguez⁹, Jong Woo Lee¹⁴, Mohammad Tabaeizadeh^1,2, Emily J. Gilmore⁸, Kristy Nordstrom¹, Ji Yeoun Yoo²², Manisha Holmes²³, Susan T. Herman²⁴, Jennifer A. Williams²⁵, Jay Pathmanathan²⁶, Fábio A. Nascimento^1,2, Ziwei Fan^1,2, Samaneh Nasiri^1,2, Mouhsin M. Shafi²⁷, Sydney S. Cash^1,2, Daniel B. Hoch^1,2, Andrew J. Cole^1,2, Eric S. Rosenthal^1,2, Sahar F. Zafar^1,2, Jimeng Sun⁵, M. Brandon Westover^1,2
金京，温东戈，Aaron F. Struck，Marta Bento Fernandes，洪申大，安成泰，萨弗拉·法蒂玛，艾琳·赫洛皮安，伊奥尼斯·卡拉基斯，乔纳森·J·哈尔福德，马库斯·吴，艾米莉·L·约翰逊，布莱恩·阿帕沃，拉尼·A·萨基斯，加马勒阿尔迪恩奥斯曼，彼得·W·卡普兰，莫妮卡·B·达卡，拉克什曼·阿科特·贾亚戈帕尔，祖贝达·谢赫，奥尔哈·塔拉斯琴科，莎拉·施密特，希巴·A·海德，詹妮弗·A·金，克里斯塔·B·斯威塞，尼古拉斯·加斯帕德，麦肯齐·C·切尔文卡，安德烈斯·罗德里格斯，李钟雨，穆罕默德·塔巴伊扎德，艾米莉·J·吉尔莫，克里斯蒂·诺德斯特龙，刘智润，Manisha Holmes，苏珊·T·赫尔曼，詹妮弗·A·威廉姆斯，杰伊·帕特曼纳坦，法比奥·A·纳西门托，范子崴，萨玛内·纳西里，穆辛·M·沙菲，雪梨S. 现金 ^1,2 ，Daniel B. Hoch ^1,2 ，Andrew J. Cole ^1,2 ，Eric S. Rosenthal ^1,2 ，Sahar F. Zafar ^1,2 ，Jimeng Sun ⁵ ，M.布兰登·韦斯托弗 ^1,2

1-Massachusetts General Hospital/Harvard Medical School Department of Neurology, MA
马萨诸塞州总医院/哈佛医学院神经科学系，马萨诸塞州
2-Massachusetts General Hospital Clinical Data Animation Center (CDAC), MA
马萨诸塞州总医院临床数据动画中心（CDAC），马萨诸塞州
3-University of Wisconsin-Madison Department of Neurology
3-威斯康星大学麦迪逊分校神经学系
4-William S Middleton Memorial Veterans Hospital Madison, WI
4-威廉·S·米德尔顿纪念退伍军人医院，威斯康星州麦迪逊市
5-National Institute of Health Data Science, Peking University, Beijing, China
北京大学国家健康数据科学研究院
6-University of Illinois at Urbana-Champaign, College of Computing, Champaign, IL
伊利诺伊大学厄巴纳-香槟分校，计算机学院，伊利诺伊州香槟市
7-Georgia Institute of Technology, College of Computing, Atlanta, GA
7-佐治亚理工学院，计算机学院，亚特兰大，佐治亚州
8-Yale University-Yale New Haven Hospital, CT
8-耶鲁大学-康涅狄格州耶鲁纽黑文医院
9-Emory University School of Medicine, GA
9-埃默里大学医学院（位于佐治亚州）
10-Medical University of South Carolina, SC
南卡罗来纳州医科大学
11-University of Manitoba, Canada
11-曼尼托巴大学，加拿大
12-Johns Hopkins School of Medicine, MD
12-约翰霍普金斯医学院，MD
13-University of Arizona College of Medicine, AZ
亚利桑那大学医学院
14-Brigham and Women's Hospital, MA
14-布里格姆妇女医院，马萨诸塞州
15-Mayo Clinic-Rochester, MN
15-梅奥诊所-罗切斯特，明尼苏达州
16-Warren Alpert School of Medicine of Brown University, Providence, RI
布朗大学沃伦·阿尔珀特医学院，位于罗得岛普罗维登斯市
17-University of Nebraska Medical Center, NE
17-内布拉斯加医学中心，内布拉斯加州
18-West Virginia University Hospitals, WV
18-西弗吉尼亚大学医院，WV
19-University of Chicago, Chicago, IL
芝加哥大学，伊利诺伊州芝加哥市
20-Atrium Health, NC 20-心脏健康，北卡罗来纳州
21-Université Libre de Bruxelles - Hôpital Erasme, Belgium
21-比利时布鲁塞尔自由大学 - 埃拉斯姆斯医院
22-Icahn School of Medicine, Mount Sinai, NY
伊坎医学院，西奈山，纽约州
23-New York University (NYU) Grossman School of Medicine, NY
23-纽约大学（NYU）格罗斯曼医学院，纽约
24-Barrow Neurological Institute, Phoenix, AZ
24-巴罗神经研究所，亚利桑那州凤凰城
25-Mater Misericordiae University Hospital, Dublin, Ireland.
25-爱尔兰都柏林慈悲之母大学医院。
26-University of Pennsylvania, PA
26-宾夕法尼亚大学，宾夕法尼亚州
27-Beth Israel Deaconess Medical Center/Harvard Medical School, MA
27-贝斯以色列执事医疗中心/哈佛医学院，马萨诸塞州